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Found 10 papers, 1 papers with code
Bayesian Decision Trees Inspired from Evolutionary Algorithms
Bayesian Decision Trees (DTs) are generally considered a more advanced and accurate model than a regular Decision Tree (DT) because they can handle complex and uncertain data.
Parallel Approaches to Accelerate Bayesian Decision Trees
None-the-less, we propose two methods for exploiting parallelism in the MCMC: in the first, we replace the MCMC with another numerical Bayesian approach, the Sequential Monte Carlo (SMC) sampler, which has the appealing property that it is an inherently parallel algorithm; in the second, we consider data partitioning.
Adaptive Bayesian Beamforming for Imaging by Marginalizing the Speed of Sound
Imaging methods based on array signal processing often require a fixed propagation speed of the medium, or speed of sound (SoS) for methods based on acoustic signals.
Can We Automate the Analysis of Online Child Sexual Exploitation Discourse?
Social media's growing popularity raises concerns around children's online safety.
Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter
This paper proposes a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter to lower its computational complexity and make it suitable for multiple target tracking with a high number of targets.
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters
It has been widely documented that the sampling and resampling steps in particle filters cannot be differentiated.
An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA
On the contrary, as a consequence of the spooky effect at a distance in optimal OSPA/UOSPA estimation, the optimal actions for different sensors using OSPA and UOSPA are entangled.
Reliability Validation of Learning Enabled Vehicle Tracking
However, it is unclear if and how the adversarial examples over learning components can affect the overall system-level reliability.
Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)
This paper proposes an approach to detect moving objects in Wide Area Motion Imagery (WAMI), in which the objects are both small and well separated.
Convolutional Neural Networks for Aerial Vehicle Detection and Recognition
This paper investigates the problem of aerial vehicle recognition using a text-guided deep convolutional neural network classifier.
